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amazon-product-research-mcp

unauthorized_sellers

Read-only

List the sellers winning a brand's buy box — the resellers and arbitrage operators you're up against — each classified (authorized-retailer / arbitrage / Amazon / brand-direct / reseller). If you've saved an authorized list (authorized_seller_set) it instead flags the UNAUTHORIZED sellers. Use when the user asks 'which operators dominate the buy box on ', 'who else is selling my brand', 'unauthorized sellers on Nike', 'find rogue sellers', or any brand buy-box / protection question.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
brandYesBrand name (case-insensitive).
limitNo
seller_nameNoExact seller name (case-insensitive).
last_seen_toNo
first_seen_toNo
max_avg_priceNo
min_avg_priceNo
last_seen_fromNo
marketplace_idNo1 = Amazon UK, 2 = Amazon US (default), 4 = Amazon CA, 5 = Amazon AU, 6 = Amazon DE, 7 = Amazon JP, 8 = Amazon IT, 9 = Amazon FR, 10 = Amazon ES, 11 = Amazon MX, 12 = Amazon BR
first_seen_fromNoYYYY-MM-DD.
operator_type_inNoComma-separated classifications to keep (cold path only, when no authorized list is set): e.g. arbitrage, reseller, amazon, brand-direct, authorized-retailer.
max_asins_touchedNo
min_asins_touchedNo
authorized_sellersNoOptional. Authorized seller names — sellers NOT in this list are flagged. If omitted (and none saved), all buy-box-winning sellers are returned, classified.
seller_name_containsNo
max_observed_buybox_daysNo
min_observed_buybox_daysNo

TDQS

A3.9/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds meaningful behavioral detail: it returns classified buy-box winners, and if an authorized_seller_set is saved it instead flags unauthorized sellers. This goes beyond the structured fields and clarifies the two operational modes.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences with the core function front-loaded and the conditional behavior and usage examples following efficiently. The list of example queries is somewhat long but directly useful for an agent matching user intent.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description provides strong purpose, usage, and conditional behavior context, but with 17 parameters, 35% schema coverage, and no output schema it is not fully complete. An agent could select and invoke it correctly for the main use case, but optional filters and return shape remain underspecified.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is only 35%, and the description does not compensate by explaining the many filter parameters such as limit, date ranges, price bounds, asins_touched, or buybox_days. It only touches the brand and authorized-sellers concept, leaving most of the 17 parameters underdocumented.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb and resource: 'List the sellers winning a brand's buy box' and explains the classification categories. It also distinguishes the tool's behavior from related sibling tools like authorized_seller_list by describing the conditional 'UNAUTHORIZED sellers' mode.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit usage triggers with example user phrasings such as 'unauthorized sellers on Nike' and 'find rogue sellers', and closes with 'any brand buy-box / protection question'. It does not explicitly name sibling alternatives or state when not to use the tool, but the context is clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A3.5/5.0
Disambiguation2/5

The tool set is extremely granular, with multiple clusters that overlap in purpose (e.g., amazon_search_results/search_products/shopping_search; watchlist_delta/watchlist_diff; find_undercompeted_brands/category_undercompeted_brands; operator_new_brands/operator_new_on_brand). Although descriptions are detailed, the boundaries between many 'find opportunity' and 'watchlist change' tools are subtle enough that an agent could easily misselect.

Naming Consistency4/5

The vast majority follow a verb_noun snake_case convention with clear prefixes (asin_, brand_, category_, operator_, watchlist_, playbook_, find_, top_). A few noun-style exceptions (competitive_landscape, risk_assessment, brand_under_attack, buybox_loss_alert) break the pattern, but they are minor and do not obscure the overall scheme.

Tool Count1/5

With 82 tools, the server is far beyond the 50+ extreme threshold. Even though the domain is broad, many tools are highly granular variants (e.g., filter_brands_by_fba_share vs filter_operators_by_fba_share; watchlist_delta vs watchlist_diff) that could be merged or parameterized, imposing a heavy cognitive and context burden on agents.

Completeness5/5

The surface is extraordinarily complete for Amazon product research: discovery, ASIN/brand/category analytics, buybox and BSR history, sourcing evaluation, risk/MAP monitoring, watchlists, playbooks, operator intelligence, cross-marketplace checks, and live refreshes. Workflows like authorized_seller_set → buybox_loss_alert and watchlist_add → watchlist_delta are fully supported, with no obvious dead ends.